A system context-aware approach for battery lifetime prediction in smart phones

Authors:
Xia Zhao;Yao Guo;Qing Feng;Xiangqun Chen
Affiliations:
Peking University, Beijing, China;Peking University, Beijing, China;Peking University, Beijing, China;Peking University, Beijing, China
Venue:
Proceedings of the 2011 ACM Symposium on Applied Computing
Year:
2011

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Abstract

Energy is a bottleneck in smart phone systems, and knowing the status of the battery lifetime and being able to use it efficiently is an important requirement from users. We propose a system context-aware approach for predicting battery lifetime, which allows a user to know the accurate battery status and to utilize the power efficiently. We refer to a collection of system component states as system context and model the quantitative relation between system context attributes and the battery discharge rate by multiple linear regressions. When the user changes applications or operations, we can dynamically predict the remaining battery lifetime as well as its variations by monitoring system context attributes. We implement the CABLI system with our approach as on an HTC G1 smart phone running the Android operating system. Experiments show that our model describes how the changes of system component states affect the battery lifetime, and that it improves the accuracy of online battery lifetime prediction.